Ferdinand Schockenhoff - Capgemini Invent - Product development 2030+

Product development 2030+

(Ferdinand Schockenhoff, Senior Manager, Digital Engineering & R&D, Capgemini Invent)

Context

• Speaker: Ferdinand Schockenhoff (introduced by Jan).
• Focus: Automotive-centered examples presented as transferable to other industries.
• Goal: Make product development faster, cheaper, higher-quality and multi-functional through AI and digitization.

Key trends motivating change

• Increasing time-to-market and cost pressure.
• Growing software content and electrification in products.
• New competitors and business models.
• Current engineering work is often manual (Office tools, phone calls) and not machine-actionable — agents cannot operate unless development artifacts are digitized and standardized.

High-level model for AI-driven product development

1. Systems engineering foundation

   • Model effect chains: capture functional and physical interactions between components and roles (who talks to whom).
   • Digitize these models so they are explicit rather than implicit in engineers’ heads.

2. Add simulations and structured data

   • Describe component or black-box behavior via simulations, databases and workflows.
   • Ensure each part has defined inputs/outputs and physical boundaries.

3. Deploy AI agents on top

   • Only after steps 1–2 can autonomous agents (multi-agent systems) meaningfully design, integrate and make decisions.
   • Example: a battery-geometry agent + vehicle-integration agent + cost/approval agent working together.

Implementation approach — combined foundation + use-case driven

Do foundational modernization in parallel with concrete AI use cases.

• Standards & methods

  • Use consistent modeling languages/methods across effect chains (e.g., SysML-type approaches).

• IT platforms

  • Prefer standardized, scalable platforms that provide data continuity and an integrated UX over one-off tools.

• Structured data

  • Choose a function- or system-oriented end-to-end data architecture and enforce it.

• Organizational alignment

  • Rethink responsibilities and create roles that enable automated/agent-driven steps.

Practical guide — steps for use-case implementation

1. Process selection

   • Identify high-impact, high-automation, repeatable engineering tasks (avoid “nice-to-have” chatbots).

2. Define clear end-to-end boundaries

   • Specify explicit start and end points for the automated task.

3. Data check

   • Confirm available input data and required outputs (e.g., CAD positions, engineering BOM → collision-free geometry + interference report).

4. Stepwise decomposition

   • Break the process into subprocesses/tasks; implement each as an agent where sensible.

5. Enablement

   • Train and onboard engineers and process owners; adjust organization and governance.

6. Scale

   • Chain agents into multi-agent systems; reuse and recycle agents across use cases.

Example use case: Battery geometry clearance check

• Inputs: data models (positions), engineering BOM.
• Agent tasks:
  • Run geometry/clearance checks.
  • Generate interference report.
  • Pass results to integration, cost, and approval agents.
• Output: collision-free geometry validated for manufacturing.

AI Factory / Platform concepts

• Build foundational tooling: platforms, sandboxes, infrastructure, and libraries of reusable agents/components.
• Embed governance, legal, compliance, and AI regulation into the strategy.
• Invest in upskilling across product development so agents are actually used and produce business impact.

“Agentic AI is arriving quickly; organizations that delay risk falling behind.”

Main recommendations / messages

• Combine foundational modernization with quick, high-impact use cases to demonstrate value and steer transformation.
• Focus on the processes engineers actually perform (value‑stream oriented).
• Without digitization and structured data, AI agents cannot be effective.
• Act fast to capture advantage from emerging agentic AI capabilities.

Main speakers / sources

• Ferdinand Schockenhoff — Senior Manager, Digital Engineering & R&D, Capgemini Invent
• Moderator / introducer: Jan (name not given in transcript)
• Organization referenced: Capgemini Invent